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“As a design and marketing partner to millions of small businesses worldwide, Vista has always prioritized customer experience. Level AI’s agent screen recording has added “eyes” to a process where we only had “ears” before. This has helped us identify opportunities to improve our processes and tools and coach our agents more effectively, which improves both team member satisfaction and customer experience.”

Michael Villanueva

Global Director of Quality - Vista

“As a design and marketing partner to millions of small businesses worldwide, Vista has always prioritized customer experience. Level AI’s agent screen recording has added “eyes” to a process where we only had “ears” before. This has helped us identify opportunities to improve our processes and tools and coach our agents more effectively, which improves both team member satisfaction and customer experience.”

Michael Villanueva

Global Director of Quality - Vista

“As a design and marketing partner to millions of small businesses worldwide, Vista has always prioritized customer experience. Level AI’s agent screen recording has added “eyes” to a process where we only had “ears” before. This has helped us identify opportunities to improve our processes and tools and coach our agents more effectively, which improves both team member satisfaction and customer experience.”

Michael Villanueva

Global Director of Quality - Vista

“We’re able to get so much intel about our calls and our customers that it really helped us make changes in our processes to convert more calls. We’ve gone from manually scoring 1-2% of our calls to using Level AI to score 100% of our calls!”

Angela Zander

Director of Operations - Quinstreet

“We’re able to get so much intel about our calls and our customers that it really helped us make changes in our processes to convert more calls. We’ve gone from manually scoring 1-2% of our calls to using Level AI to score 100% of our calls!”

Angela Zander

Director of Operations - Quinstreet

“We’re able to get so much intel about our calls and our customers that it really helped us make changes in our processes to convert more calls. We’ve gone from manually scoring 1-2% of our calls to using Level AI to score 100% of our calls!”

Angela Zander

Director of Operations - Quinstreet

FAQ

Frequently Asked Questions

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1. What is call center quality assurance software?

Call center quality assurance software is a platform that reviews, scores, and reports on customer interactions such as calls, chats, and emails against defined quality standards. Traditionally, QA teams manually reviewed a small sample of interactions, but AI-powered QA software can automatically evaluate up to 100% of conversations. With Level AI, contact centers can automate QA at scale while maintaining the flexibility to evaluate interactions against their own quality criteria. This makes it easier to monitor performance, identify trends, and maintain consistent customer experiences as call volumes grow.

2. Can we keep our existing QA scorecard, or do we have to rebuild it?

You can keep your existing QA scorecard, including scorecards used for external reporting or established QA processes. However, adapting your scorecards for AI-powered QA can help you get more value from automation. Level AI can automate almost any custom scorecard, including complex and organization-specific questions, allowing teams to automate far more of their existing QA process without having to completely redesign their evaluation framework.

3. How do we combine auto-scored and manually scored questions on the same scorecard?

Level AI supports hybrid scorecards that combine AI-scored and human-evaluated questions in a single scorecard. Objective, rules-based questions, such as whether an agent gave the required greeting or verified a customer's identity, can be scored automatically. More subjective questions, such as empathy or rapport, can be evaluated by a human when needed. Both sets of answers roll up into a single score, allowing teams to automate what can be automated while keeping human judgment where it matters.

4. What happens when we disagree with an AI score? Can we correct or retrain it?

Yes. Evaluators can override an AI-generated score when they disagree with an evaluation. These corrections can then be used to improve and recalibrate future scoring, helping the system better align with how your team evaluates conversations. This feedback loop allows QA teams to continuously improve scoring accuracy instead of treating AI evaluation as a one-time setup.

5. Do humans still review the AI's evaluations? What does that team look like?

Yes. Human oversight remains an important part of AI-powered QA, but the role of the QA team changes. Evaluators can review AI-generated evaluations, resolve disagreements, calibrate criteria, and coach agents based on the trends AI identifies. Humans can also evaluate interactions themselves with AI guidance: Level AI surfaces suggested answers along with relevant timestamps and conversation quotes, helping evaluators find the evidence they need and complete manual reviews much faster. This lets QA teams spend less time searching through conversations and more time on higher-value evaluation and coaching.

6. Can we set questions to N/A based on conditions?

Yes. Level AI supports conditional logic that can automatically mark specific scorecard questions as not applicable based on factors such as call type, department, or responses to earlier questions. For example, a billing dispute call does not need to be evaluated on whether an agent delivered a sales pitch. Conditional logic keeps evaluations fair and relevant across different interaction types without requiring teams to create separate scorecards for every scenario.

7. What percentage of calls should be monitored for quality assurance?

With manual QA, many teams can review only a small percentage of interactions, often around 1% to 3%, because of time and resource constraints. This leaves most customer interactions unmeasured. With Level AI, teams can automatically evaluate up to 100% of conversations, providing a much more complete view of agent performance, compliance, and customer experience instead of relying on a small sample that may not represent overall performance.

8. How do we transition from our current QA process without disrupting our reporting?

Level AI gives teams tools to test and validate their QA rubrics before fully deploying them, so you do not have to rely on a lengthy parallel rollout simply to determine whether your scorecards work. Admins can run AutoQA rubrics against real conversations in a sandbox, verify scoring accuracy, and rapidly test different question prompts while building and refining rubrics. This makes it possible to validate your AI-powered QA process before changing your existing workflow or reporting.

9. How accurate is AI-based QA scoring?

The accuracy of AI-based QA depends on how well the evaluation criteria and scoring logic are configured. When properly calibrated, AI can deliver highly consistent scoring because it applies the same criteria across every interaction without the variability caused by factors such as reviewer fatigue, workload, or differences in interpretation. Level AI also provides tools for testing and refining AutoQA rubrics against real conversations, helping teams validate accuracy and continuously improve their scoring.

10. What should you look for in call center QA software?

Look for call center QA software that provides full conversation coverage, flexible custom scorecards, automated and manual evaluation options, conditional logic, score overrides, and strong reporting capabilities. It should also make it easy to test and validate AI scoring before deployment and support your existing QA workflows. Beyond QA scores, look for the ability to connect quality data with business outcomes such as CSAT, compliance, and revenue. Integration with your existing contact center platforms is also important for making QA part of your broader quality and customer experience workflow.